PDF(Pubmed)
Abstract：
Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be difficult due to partial occlusion and environmental reflections. Although recent efforts to use machine learning (ML) for pupil tracking have demonstrated superior results when evaluated using standard measures of segmentation performance, little is known of how these networks may affect the quality of the final gaze estimate. This work provides an objective assessment of the impact of several contemporary ML-based methods for eye feature tracking when the subsequent gaze estimate is produced using either feature-based or model-based methods. Metrics include the accuracy and precision of the gaze estimate, as well as drop-out rate.
摘要：
用于估计来自移动和基于视频的眼睛跟踪器的注视方向的算法通常涉及以与移动的注视方向一致的方式跟踪通过眼睛相机图像移动的眼睛的特征。例如瞳孔的中心或边界。由于部分遮挡和环境反射，使用传统的计算机视觉技术跟踪这些特征可能很困难。尽管最近使用机器学习（ML）进行瞳孔跟踪的努力在使用分割性能的标准度量进行评估时已经证明了出色的结果，人们对这些网络如何影响最终凝视估计的质量知之甚少。当使用基于特征或基于模型的方法产生后续凝视估计时，这项工作提供了对几种当代基于ML的方法对眼睛特征跟踪的影响的客观评估。度量包括凝视估计的准确性和精确度，以及辍学率。
